{"corpus_id":215738707,"paper_sha":"b092d1ae7e5d774f4d3b2902d18bd6a916d14cdd","doi":"10.1109/ICSIoT47925.2019.00014","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":3015447433,"dblp_id":null,"acl_id":null,"title":"Detecting Cyber Supply Chain Attacks on Cyber Physical Systems Using Bayesian Belief Network","year":2019,"publication_date":"2019-05-01","venue":"2019 International Conference on Cyber Security and Internet of Things (ICSIoT)","journal":{"name":"2019 International Conference on Cyber Security and Internet of Things (ICSIoT)","pages":"37-42","volume":null},"journal_issn":null,"journal_title":null,"publication_types":["Conference"],"pubmed_pub_types":null,"s2_fields_of_study":["Computer Science","Engineering"],"reference_count":26,"citation_count":18,"influential_citation_count":0,"is_open_access":true,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":"http://repository.uwl.ac.uk/id/eprint/8027/1/Detecting%20Cyber%20Supply%20Chain%20Attacks%20on%20Cyber%20Physical%20Systems%20Using%20Bayesian%20Belief%20Network.pdf","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/b092d1ae7e5d774f4d3b2902d18bd6a916d14cdd","s2_open_access_license":null,"s2_open_access_status":"GREEN","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.","claims":[{"public_id":"cl_711bdefbc391d2dbc1468b54570d325a","status":"active","text":"A smart grid case study is used to demonstrate the applicability of the attack model and its cascading effects.","confidence":0.88,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_711bdefbc391d2dbc1468b54570d325a"},{"public_id":"cl_6aff96eaaa20965b3eaa6480e1544f78","status":"active","text":"Bayesian belief networks can be adapted to determine uncertainties in the event of cyberattacks in the cyber supply chain domain.","confidence":0.93,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous 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